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In computing, a hash table is a data structure that implements an associative array, also called a dictionary or simply map; an associative array is an abstract data type that maps keys to values. [2] A hash table uses a hash function to compute an index, also called a hash code, into an array of buckets or slots, from which the desired value ...
It is also well suited for implementing a concurrent hash table. Hopscotch hashing was introduced by Maurice Herlihy, Nir Shavit and Moran Tzafrir in 2008. [1] The name is derived from the sequence of hops that characterize the table's insertion algorithm (see Hopscotch for the children's game). The algorithm uses a single array of n buckets.
Although std::map is typically implemented using a self-balancing binary search tree, C++11 defines a second map called std::unordered_map, which has the algorithmic characteristics of a hash table. This is a common vendor extension to the Standard Template Library (STL) as well, usually called hash_map , available from such implementations as ...
The values are usually used to index a fixed-size table called a hash table. Use of a hash function to index a hash table is called hashing or scatter-storage addressing. Hash functions and their associated hash tables are used in data storage and retrieval applications to access data in a small and nearly constant time per retrieval.
Linear probing is a component of open addressing schemes for using a hash table to solve the dictionary problem.In the dictionary problem, a data structure should maintain a collection of key–value pairs subject to operations that insert or delete pairs from the collection or that search for the value associated with a given key.
For any fixed set of keys, using a universal family guarantees the following properties.. For any fixed in , the expected number of keys in the bin () is /.When implementing hash tables by chaining, this number is proportional to the expected running time of an operation involving the key (for example a query, insertion or deletion).
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For prime m > 2, most choices of c 1 and c 2 will make h(k,i) distinct for i in [0, (m−1)/2]. Such choices include c 1 = c 2 = 1/2, c 1 = c 2 = 1, and c 1 = 0, c 2 = 1. However, there are only m /2 distinct probes for a given element, requiring other techniques to guarantee that insertions will succeed when the load factor exceeds 1/2.